Steganalysis of video sequences using collusion sensitivity

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Abstract

In this thesis we present an effective steganalysis technique for digital video sequences
based on the collusion attack. Steganalysis is the process of detecting with a high probability
the presence of covert data in multimedia. Existing algorithms for steganalysis target
detecting covert information in still images. When applied directly to video sequences
these approaches are suboptimal. In this thesis we present methods that overcome this
limitation by using redundant information present in the temporal domain to detect covert
messages in the form of Gaussian watermarks. In particular we target the spread spectrum
steganography method because of its widespread use. Our gains are achieved by exploiting
the collusion attack that has recently been studied in the field of digital video watermarking
and more sophisticated pattern recognition tools. Through analysis and simulations we,
evaluate the effectiveness of the video steganalysis method based on averaging based collusion
scheme. Other forms of collusion attack in the form of weighted linear collusion and
block-based collusion schemes have been proposed to improve the detection performance.
The proposed steganalsyis methods were successful in detecting hidden watermarks
bearing low SNR with high accuracy. The simulation results also show the improved performance
of the proposed temporal based methods over the spatial methods. We conclude
that the essence of future video steganalysis techniques lies in the exploitation of the temporal
redundancy.